Deterministic processes drive wetland plant community composition and diversity

Background

Data Collection

Data Configuration

We have species composition data from 1031 1m^2 quadrats representing 27 sites.

Nearly all statistical packages require the data to be in a presence-absence form. There are several ways to do it (one of which can maintain cover values rather than changing it to binary data); I used a loop function. The result is a presence-absence matrix with Site as a column so subsamples can be organized accordingly. This column must be deleted for every analysis, however!

Table 1. Sample of data in presence-absence format.

	Site	SCICYP	PROPEC	JUNDEB	JUNREP	CXJOOR	CXLOUI
1.1.1	1	1	1	1	1	1	0
1.1.2	1	1	1	1	0	0	1
1.1.3	1	1	1	0	0	0	1
1.1.4	1	1	1	0	0	1	1
1.1.5	1	0	0	0	0	0	1

Diversity Estimates

Sites had widely varying observed total richness. The use of extrapolated species accumulation curves can tell us how many species are likely to be at the site based on how many were found in accumulating subsamples. However if a curve never flattens, it gives a wild estimate of richness (see Site 10).

What do the curves that made these estimates look like? Let’s take a look!

This plot is not particularly helpful other than to visualize the general span of observed and expected richnesses and sampling efforts. Examining the curves in portions of 3-4 is necessary to

These curves illustrate not only where the flattening point (expected richness) occurs, but also how quickly. Examining a curve can allow someone to estimate how many more samples would be needed to reach that point, however if doing so samples a larger area then the curve may never flatten.

Let’s see if sampling effort (# quadrats/area sampled) affected percent estimated sampling completion; if it did, that would be a big problem and I would have a lot of explaining to do to my committee.

There is no relationship between sampling effort and completion percentage (p=0.6366006). However, note that Sites 10 and 26 were flagged as outliers by the autoplot function. This inadequate sampling is likely the result of too few quadrats sampled at the wetland edge relative to the size of the wetland.

Effects on Diversity

There appears to be a relationship between site richness and site area, but unexpectedly this relationship appears to be negative. Because the data are likely non-linear, a generalized linear model should be used to assess this relationship.

Community Classification

Which sites are similar?

There may be a lot of overlap in clusters due to the nestedness of some community types. We should revisit this later using quadrats as replicates.

Are sites similar because they’re geographically closer?

ggplot(distance.f, aes(x=distance.f$geographic, y=distance.f$Sorensen)) +
  geom_point()+
  geom_smooth(method="glm", method.args=list(family="binomial"(link="logit")))+
  ylab("Sorensen dissimilarity index")+
  xlab("Geographic distance (m)")+
  ggtitle("Geographic distance between sites has no effect on species similarity")+
  coord_cartesian(ylim = c(0, 1))

non-integer #successes in a binomial glm!

Call:
glm(formula = Sorensen ~ geographic, family = binomial, data = distance.f)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.13785  -0.13608   0.02589   0.19654   0.64852  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) 1.427e+00  1.626e-01   8.777   <2e-16 ***
geographic  6.933e-07  1.764e-06   0.393    0.694    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 43.121  on 701  degrees of freedom
Residual deviance: 42.966  on 700  degrees of freedom
AIC: 298.02

Number of Fisher Scoring iterations: 4

Ordinations

Implications

Plant communities aren’t real. We can all go home now.